Deep learning inference of miRNA expression from bulk and single-cell mRNA expression

Studying miRNA activity at the single-cell level presents a significant challenge due to the limitations of existing single-cell technologies in capturing miRNAs. To address this, we introduce two deep learning models: Cross-modality (CM) and single-modality (SM), both based on encoder-decoder archi...

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Vydané v:Journal of bioinformatics and computational biology Ročník 23; číslo 3; s. 2550009
Hlavní autori: Ripan, Rony Chowdhury, Athaya, Tasbiraha, Li, Xiaoman, Hu, Haiyan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Singapore 01.06.2025
Predmet:
Algorithms
Computational Biology - methods
Deep Learning
Gene Expression Profiling - methods
Humans
MicroRNAs - genetics
MicroRNAs - metabolism
RNA, Messenger - genetics
RNA, Messenger - metabolism
Single-Cell Analysis - methods
deep learning
miRNA expression
cross-modality
encoder-decoder
ISSN:1757-6334, 1757-6334
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Abstract Studying miRNA activity at the single-cell level presents a significant challenge due to the limitations of existing single-cell technologies in capturing miRNAs. To address this, we introduce two deep learning models: Cross-modality (CM) and single-modality (SM), both based on encoder-decoder architectures. These models predict miRNA expression at both bulk and single-cell levels using mRNA data. We evaluated the performance of CM and SM against the state-of-the-art miRSCAPE approach, using both bulk and single-cell datasets. Our results demonstrate that both CM and SM outperform miRSCAPE in accuracy. Furthermore, incorporating miRNA target information substantially enhanced performance compared to models that utilized all genes. These models provide powerful tools for predicting miRNA expression from single-cell mRNA data.
AbstractList Studying miRNA activity at the single-cell level presents a significant challenge due to the limitations of existing single-cell technologies in capturing miRNAs. To address this, we introduce two deep learning models: Cross-modality (CM) and single-modality (SM), both based on encoder-decoder architectures. These models predict miRNA expression at both bulk and single-cell levels using mRNA data. We evaluated the performance of CM and SM against the state-of-the-art miRSCAPE approach, using both bulk and single-cell datasets. Our results demonstrate that both CM and SM outperform miRSCAPE in accuracy. Furthermore, incorporating miRNA target information substantially enhanced performance compared to models that utilized all genes. These models provide powerful tools for predicting miRNA expression from single-cell mRNA data.Studying miRNA activity at the single-cell level presents a significant challenge due to the limitations of existing single-cell technologies in capturing miRNAs. To address this, we introduce two deep learning models: Cross-modality (CM) and single-modality (SM), both based on encoder-decoder architectures. These models predict miRNA expression at both bulk and single-cell levels using mRNA data. We evaluated the performance of CM and SM against the state-of-the-art miRSCAPE approach, using both bulk and single-cell datasets. Our results demonstrate that both CM and SM outperform miRSCAPE in accuracy. Furthermore, incorporating miRNA target information substantially enhanced performance compared to models that utilized all genes. These models provide powerful tools for predicting miRNA expression from single-cell mRNA data.
Studying miRNA activity at the single-cell level presents a significant challenge due to the limitations of existing single-cell technologies in capturing miRNAs. To address this, we introduce two deep learning models: Cross-modality (CM) and single-modality (SM), both based on encoder-decoder architectures. These models predict miRNA expression at both bulk and single-cell levels using mRNA data. We evaluated the performance of CM and SM against the state-of-the-art miRSCAPE approach, using both bulk and single-cell datasets. Our results demonstrate that both CM and SM outperform miRSCAPE in accuracy. Furthermore, incorporating miRNA target information substantially enhanced performance compared to models that utilized all genes. These models provide powerful tools for predicting miRNA expression from single-cell mRNA data.
Author Ripan, Rony Chowdhury
Hu, Haiyan
Li, Xiaoman
Athaya, Tasbiraha
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SubjectTerms Algorithms
Computational Biology - methods
Deep Learning
Gene Expression Profiling - methods
Humans
MicroRNAs - genetics
MicroRNAs - metabolism
RNA, Messenger - genetics
RNA, Messenger - metabolism
Single-Cell Analysis - methods
Title Deep learning inference of miRNA expression from bulk and single-cell mRNA expression
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